Machine Learning based Slow Learner Prediction in Educational Sector

Nurun Nahar Ela, Nusrat Jahan
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Abstract

The educational sector has been proved to be a major sector where improvement measures are a must to develop the system and the curriculum which changes in every few years. Students state that in order to cope with the changing curriculum every now and then, Education is said to be the backbone of a nation. If the students are falling behind that means the nation is falling behind. Therefore, it is necessary to guide student for their betterment which will help us to achieve a strong backbone. Our purpose of the study is to predict the slow learner among the university level learners which is the crucial stage of their study life and the step where they must acquire skills to face the professional life. The proposed study has collected data from the computer science and engineering department students. In order to achieve the better outcome, machine learning algorithms have been applied and finally 98% accuracy has been obtained.
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基于机器学习的教育领域慢学习者预测
事实证明,教育部门是一个必须采取改进措施的主要部门,以发展每隔几年就会改变的制度和课程。学生们表示,为了适应不断变化的课程,教育被认为是一个国家的支柱。如果学生落后了,那就意味着国家落后了。因此,有必要引导学生为他们的进步,这将有助于我们实现强大的骨干。我们的研究目的是预测大学水平学习者中的慢学习者,这是他们学习生活的关键阶段,也是他们必须掌握面对职业生活的技能的步骤。这项拟议的研究收集了计算机科学与工程系学生的数据。为了达到更好的效果,应用了机器学习算法,最终达到了98%的准确率。
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